dask.array.nancumprod
dask.array.nancumprod¶
- dask.array.nancumprod(x, axis, dtype=None, out=None, *, method='sequential')[source]¶
Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones.
This docstring was copied from numpy.nancumprod.
Some inconsistencies with the Dask version may exist.
Dask added an additional keyword-only argument
method
.- method{‘sequential’, ‘blelloch’}, optional
Choose which method to use to perform the cumprod. Default is ‘sequential’.
‘sequential’ performs the cumprod of each prior block before the current block.
- ‘blelloch’ is a work-efficient parallel cumprod. It exposes parallelism by first
taking the product of each block and combines the products via a binary tree. This method may be faster or more memory efficient depending on workload, scheduler, and hardware. More benchmarking is necessary.
Ones are returned for slices that are all-NaN or empty.
New in version 1.12.0.
- Parameters
- aarray_like (Not supported in Dask)
Input array.
- axisint, optional
Axis along which the cumulative product is computed. By default the input is flattened.
- dtypedtype, optional
Type of the returned array, as well as of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used instead.
- outndarray, optional
Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type of the resulting values will be cast if necessary.
- Returns
- nancumprodndarray
A new array holding the result is returned unless out is specified, in which case it is returned.
See also
numpy.cumprod
Cumulative product across array propagating NaNs.
isnan
Show which elements are NaN.
Examples
>>> np.nancumprod(1) array([1]) >>> np.nancumprod([1]) array([1]) >>> np.nancumprod([1, np.nan]) array([1., 1.]) >>> a = np.array([[1, 2], [3, np.nan]]) >>> np.nancumprod(a) array([1., 2., 6., 6.]) >>> np.nancumprod(a, axis=0) array([[1., 2.], [3., 2.]]) >>> np.nancumprod(a, axis=1) array([[1., 2.], [3., 3.]])